Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Social Serendipity: Mobilizing Social Software
IEEE Pervasive Computing
IEEE Transactions on Knowledge and Data Engineering
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
Co-Presence Communities: Using Pervasive Computing to Support Weak Social Networks
WETICE '06 Proceedings of the 15th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises
Exploring social context with the wireless rope
OTM'06 Proceedings of the 2006 international conference on On the Move to Meaningful Internet Systems: AWeSOMe, CAMS, COMINF, IS, KSinBIT, MIOS-CIAO, MONET - Volume Part I
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This paper proposes a social recommendation algorithm for use in a research social network environment. The social recommendation algorithm proposed combines the concepts of a relationship ontology and item-based collaborative filtering (CF). While the network setup in social networking sites can accurately reflect the social landscape of its users, it is much harder to detect the importance or strength of any one link. We therefore propose an extension to our recommendation algorithm which makes use of the idea of co-presence communities to increase the relevance of the recommendations. A copresence community can be detected from with data collected from Bluetooth-enabled mobiles. Detection of a copresence community can help determine the nature and importance of the social links between participating members